In [9]:
sns.heatmap(ETO[columns_with_missing_data_ETO].isnull(), cbar=False);
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
import sklearn
from prophet import Prophet
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.max_columns', None)
The last dataset, ETO, contains aggregated and transformed information of the weather stations, the variables are constructed with the pattern (except the variables date and ID_ESTACION) Variable + "Local" + period + type of aggregation.
The data:
• date: Day (Numeric)
• DewPoint: Dew point, temperature to which air must be cooled at constant pressure to reach saturation. DewPoint is also an indirect measure of air humidity (Degrees Kelvin).
• Evapotranspiration: Evapotranspiration of the reference crop. This is a rate that gives the amount of water lost by a reference crop (mm/h).
• FeelsLike: Heat sensation. Apparent temperature resulting from the combination of temperature, humidity and wind speed (Degrees Kelvin).
• GlobalHorizontalIrrandiance: Total amount of solar radiation received on a horizontal surface (W/m2).
• Gust: Maximum wind gust speed recorded during the observation period (m/s).
• MSLP: Barometric pressure (Pa).
• precipAmount: Hourly rainfall volume (mm/h).
• relativeHumidity: Relative humidity (%).
• SnowAmount: Hourly snowfall volume (m/h).
• Temperature: Ambient temperature at 2 metres above the ground (Kelvin degrees).
• uvIndex: Ultraviolet radiation: 0-2 = low / 3-5 = moderate / 6-7 = high / 8-10 = very high / 11-16 = extreme.
• visibility: Horizontal visibility from the weather station, 999 equals unlimited (m).
• windSpeed: Wind speed (m/s).
• ID_ESTACION: Identifier of the weather station.
Aggregations:
• Min: Minimum of the period.
• Avg: Average of the period.
• Max: Maximum of the period.Import and brief exploration of the dataset
ETO = pd.read_csv('./Datos_Originales/DATOS_ETO.TXT',sep = '|', na_values = 'NA', encoding='UTF-8')
ETO
| date | DewpointLocalAfternoonAvg | DewpointLocalAfternoonMax | DewpointLocalAfternoonMin | DewpointLocalDayAvg | DewpointLocalDayMax | DewpointLocalDayMin | DewpointLocalDaytimeAvg | DewpointLocalDaytimeMax | DewpointLocalDaytimeMin | DewpointLocalEveningAvg | DewpointLocalEveningMax | DewpointLocalEveningMin | DewpointLocalMorningAvg | DewpointLocalMorningMax | DewpointLocalMorningMin | DewpointLocalNighttimeAvg | DewpointLocalNighttimeMax | DewpointLocalNighttimeMin | DewpointLocalOvernightAvg | DewpointLocalOvernightMax | DewpointLocalOvernightMin | EvapotranspirationLocalAfternoonAvg | EvapotranspirationLocalAfternoonMax | EvapotranspirationLocalAfternoonMin | EvapotranspirationLocalDayAvg | EvapotranspirationLocalDayMax | EvapotranspirationLocalDayMin | EvapotranspirationLocalDaytimeAvg | EvapotranspirationLocalDaytimeMax | EvapotranspirationLocalDaytimeMin | EvapotranspirationLocalEveningAvg | EvapotranspirationLocalEveningMax | EvapotranspirationLocalEveningMin | EvapotranspirationLocalMorningAvg | EvapotranspirationLocalMorningMax | EvapotranspirationLocalMorningMin | EvapotranspirationLocalNighttimeAvg | EvapotranspirationLocalNighttimeMax | EvapotranspirationLocalNighttimeMin | EvapotranspirationLocalOvernightAvg | EvapotranspirationLocalOvernightMax | EvapotranspirationLocalOvernightMin | FeelsLikeLocalAfternoonAvg | FeelsLikeLocalAfternoonMax | FeelsLikeLocalAfternoonMin | FeelsLikeLocalDayAvg | FeelsLikeLocalDayMax | FeelsLikeLocalDayMin | FeelsLikeLocalDaytimeAvg | FeelsLikeLocalDaytimeMax | FeelsLikeLocalDaytimeMin | FeelsLikeLocalEveningAvg | FeelsLikeLocalEveningMax | FeelsLikeLocalEveningMin | FeelsLikeLocalMorningAvg | FeelsLikeLocalMorningMax | FeelsLikeLocalMorningMin | FeelsLikeLocalNighttimeAvg | FeelsLikeLocalNighttimeMax | FeelsLikeLocalNighttimeMin | FeelsLikeLocalOvernightAvg | FeelsLikeLocalOvernightMax | FeelsLikeLocalOvernightMin | GlobalHorizontalIrradianceLocalAfternoonAvg | GlobalHorizontalIrradianceLocalAfternoonMax | GlobalHorizontalIrradianceLocalAfternoonMin | GlobalHorizontalIrradianceLocalDayAvg | GlobalHorizontalIrradianceLocalDayMax | GlobalHorizontalIrradianceLocalDayMin | GlobalHorizontalIrradianceLocalDaytimeAvg | GlobalHorizontalIrradianceLocalDaytimeMax | GlobalHorizontalIrradianceLocalDaytimeMin | GlobalHorizontalIrradianceLocalEveningAvg | GlobalHorizontalIrradianceLocalEveningMax | GlobalHorizontalIrradianceLocalEveningMin | GlobalHorizontalIrradianceLocalMorningAvg | GlobalHorizontalIrradianceLocalMorningMax | GlobalHorizontalIrradianceLocalMorningMin | GlobalHorizontalIrradianceLocalNighttimeAvg | GlobalHorizontalIrradianceLocalNighttimeMax | GlobalHorizontalIrradianceLocalNighttimeMin | GlobalHorizontalIrradianceLocalOvernightAvg | GlobalHorizontalIrradianceLocalOvernightMax | GlobalHorizontalIrradianceLocalOvernightMin | GustLocalAfternoonAvg | GustLocalAfternoonMax | GustLocalAfternoonMin | GustLocalDayAvg | GustLocalDayMax | GustLocalDayMin | GustLocalDaytimeAvg | GustLocalDaytimeMax | GustLocalDaytimeMin | GustLocalEveningAvg | GustLocalEveningMax | GustLocalEveningMin | GustLocalMorningAvg | GustLocalMorningMax | GustLocalMorningMin | GustLocalNighttimeAvg | GustLocalNighttimeMax | GustLocalNighttimeMin | GustLocalOvernightAvg | GustLocalOvernightMax | GustLocalOvernightMin | MSLPLocalAfternoonAvg | MSLPLocalAfternoonMax | MSLPLocalAfternoonMin | MSLPLocalDayAvg | MSLPLocalDayMax | MSLPLocalDayMin | MSLPLocalDaytimeAvg | MSLPLocalDaytimeMax | MSLPLocalDaytimeMin | MSLPLocalEveningAvg | MSLPLocalEveningMax | MSLPLocalEveningMin | MSLPLocalMorningAvg | MSLPLocalMorningMax | MSLPLocalMorningMin | MSLPLocalNighttimeAvg | MSLPLocalNighttimeMax | MSLPLocalNighttimeMin | MSLPLocalOvernightAvg | MSLPLocalOvernightMax | MSLPLocalOvernightMin | PrecipAmountLocalAfternoonAvg | PrecipAmountLocalAfternoonMax | PrecipAmountLocalAfternoonMin | PrecipAmountLocalDayAvg | PrecipAmountLocalDayMax | PrecipAmountLocalDayMin | PrecipAmountLocalDaytimeAvg | PrecipAmountLocalDaytimeMax | PrecipAmountLocalDaytimeMin | PrecipAmountLocalEveningAvg | PrecipAmountLocalEveningMax | PrecipAmountLocalEveningMin | PrecipAmountLocalMorningAvg | PrecipAmountLocalMorningMax | PrecipAmountLocalMorningMin | PrecipAmountLocalNighttimeAvg | PrecipAmountLocalNighttimeMax | PrecipAmountLocalNighttimeMin | PrecipAmountLocalOvernightAvg | PrecipAmountLocalOvernightMax | PrecipAmountLocalOvernightMin | RelativeHumidityLocalAfternoonAvg | RelativeHumidityLocalAfternoonMax | RelativeHumidityLocalAfternoonMin | RelativeHumidityLocalDayAvg | RelativeHumidityLocalDayMax | RelativeHumidityLocalDayMin | RelativeHumidityLocalDaytimeAvg | RelativeHumidityLocalDaytimeMax | RelativeHumidityLocalDaytimeMin | RelativeHumidityLocalEveningAvg | RelativeHumidityLocalEveningMax | RelativeHumidityLocalEveningMin | RelativeHumidityLocalMorningAvg | RelativeHumidityLocalMorningMax | RelativeHumidityLocalMorningMin | RelativeHumidityLocalNighttimeAvg | RelativeHumidityLocalNighttimeMax | RelativeHumidityLocalNighttimeMin | RelativeHumidityLocalOvernightAvg | RelativeHumidityLocalOvernightMax | RelativeHumidityLocalOvernightMin | SnowAmountLocalAfternoonAvg | SnowAmountLocalAfternoonMax | SnowAmountLocalAfternoonMin | SnowAmountLocalDayAvg | SnowAmountLocalDayMax | SnowAmountLocalDayMin | SnowAmountLocalDaytimeAvg | SnowAmountLocalDaytimeMax | SnowAmountLocalDaytimeMin | SnowAmountLocalEveningAvg | SnowAmountLocalEveningMax | SnowAmountLocalEveningMin | SnowAmountLocalMorningAvg | SnowAmountLocalMorningMax | SnowAmountLocalMorningMin | SnowAmountLocalNighttimeAvg | SnowAmountLocalNighttimeMax | SnowAmountLocalNighttimeMin | SnowAmountLocalOvernightAvg | SnowAmountLocalOvernightMax | SnowAmountLocalOvernightMin | TemperatureLocalAfternoonAvg | TemperatureLocalAfternoonMax | TemperatureLocalAfternoonMin | TemperatureLocalDayAvg | TemperatureLocalDayMax | TemperatureLocalDayMin | TemperatureLocalDaytimeAvg | TemperatureLocalDaytimeMax | TemperatureLocalDaytimeMin | TemperatureLocalEveningAvg | TemperatureLocalEveningMax | TemperatureLocalEveningMin | TemperatureLocalMorningAvg | TemperatureLocalMorningMax | TemperatureLocalMorningMin | TemperatureLocalNighttimeAvg | TemperatureLocalNighttimeMax | TemperatureLocalNighttimeMin | TemperatureLocalOvernightAvg | TemperatureLocalOvernightMax | TemperatureLocalOvernightMin | UVIndexLocalAfternoonAvg | UVIndexLocalAfternoonMax | UVIndexLocalAfternoonMin | UVIndexLocalDayAvg | UVIndexLocalDayMax | UVIndexLocalDayMin | UVIndexLocalDaytimeAvg | UVIndexLocalDaytimeMax | UVIndexLocalDaytimeMin | UVIndexLocalEveningAvg | UVIndexLocalEveningMax | UVIndexLocalEveningMin | UVIndexLocalMorningAvg | UVIndexLocalMorningMax | UVIndexLocalMorningMin | UVIndexLocalNighttimeAvg | UVIndexLocalNighttimeMax | UVIndexLocalNighttimeMin | UVIndexLocalOvernightAvg | UVIndexLocalOvernightMax | UVIndexLocalOvernightMin | VisibilityLocalAfternoonAvg | VisibilityLocalAfternoonMax | VisibilityLocalAfternoonMin | VisibilityLocalDayAvg | VisibilityLocalDayMax | VisibilityLocalDayMin | VisibilityLocalDaytimeAvg | VisibilityLocalDaytimeMax | VisibilityLocalDaytimeMin | VisibilityLocalEveningAvg | VisibilityLocalEveningMax | VisibilityLocalEveningMin | VisibilityLocalMorningAvg | VisibilityLocalMorningMax | VisibilityLocalMorningMin | VisibilityLocalNighttimeAvg | VisibilityLocalNighttimeMax | VisibilityLocalNighttimeMin | VisibilityLocalOvernightAvg | VisibilityLocalOvernightMax | VisibilityLocalOvernightMin | WindSpeedLocalAfternoonAvg | WindSpeedLocalAfternoonMax | WindSpeedLocalAfternoonMin | WindSpeedLocalDayAvg | WindSpeedLocalDayMax | WindSpeedLocalDayMin | WindSpeedLocalDaytimeAvg | WindSpeedLocalDaytimeMax | WindSpeedLocalDaytimeMin | WindSpeedLocalEveningAvg | WindSpeedLocalEveningMax | WindSpeedLocalEveningMin | WindSpeedLocalMorningAvg | WindSpeedLocalMorningMax | WindSpeedLocalMorningMin | WindSpeedLocalNighttimeAvg | WindSpeedLocalNighttimeMax | WindSpeedLocalNighttimeMin | WindSpeedLocalOvernightAvg | WindSpeedLocalOvernightMax | WindSpeedLocalOvernightMin | ID_ESTACION | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 20150629 | 285.9 | 285.9 | 285.9 | 286.0 | 287.0 | 285.4 | 285.9 | 285.9 | 285.9 | 286.1 | 287.0 | 285.4 | NaN | NaN | NaN | 285.5 | 287.0 | 283.9 | 284.9 | 286.0 | 283.9 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 307.6 | 307.6 | 307.6 | 304.1 | 307.6 | 298.6 | 307.6 | 307.6 | 307.6 | 302.1 | 307.4 | 295.4 | NaN | NaN | NaN | 296.9 | 307.4 | 290.1 | 291.7 | 294.4 | 290.1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 27.0 | 27.0 | 27.0 | 34.3 | 47.1 | 26.3 | 27.0 | 27.0 | 27.0 | 39.3 | 57.0 | 26.3 | NaN | NaN | NaN | 52.0 | 69.7 | 26.3 | 64.7 | 69.7 | 54.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 307.6 | 307.6 | 307.6 | 304.1 | 307.6 | 298.6 | 307.6 | 307.6 | 307.6 | 302.0 | 307.4 | 295.4 | NaN | NaN | NaN | 296.9 | 307.4 | 290.1 | 291.7 | 294.4 | 290.1 | 2.0 | 2 | 2 | 0.5 | 2 | 0 | 2.0 | 2 | 2 | 0.2 | 1 | 0 | NaN | NaN | NaN | 0.1 | 1 | 0 | 0 | 0 | 0 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | NaN | NaN | NaN | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 5.2 | 5.2 | 5.2 | 4.0 | 5.2 | 2.3 | 5.2 | 5.2 | 5.2 | 3.5 | 5.0 | 2.0 | NaN | NaN | NaN | 2.6 | 5.0 | 1.1 | 1.7 | 2.1 | 1.1 | 13 |
| 1 | 20150630 | 283.0 | 283.6 | 282.5 | 284.3 | 286.5 | 282.5 | 283.2 | 283.9 | 282.5 | 285.6 | 286.0 | 285.0 | 283.5 | 283.9 | 283.0 | 285.4 | 286.0 | 283.9 | 285.1 | 286.0 | 283.9 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 308.0 | 309.0 | 305.8 | 299.2 | 309.0 | 289.6 | 302.0 | 309.0 | 289.6 | 301.3 | 307.4 | 295.7 | 296.0 | 303.6 | 289.6 | 296.9 | 307.4 | 290.8 | 292.5 | 294.6 | 290.8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6.0 | 7.1 | 4.6 | NaN | NaN | NaN | 6.0 | 7.1 | 4.6 | NaN | NaN | NaN | 6.0 | 7.1 | 4.6 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 21.9 | 24.6 | 20.1 | 43.6 | 69.7 | 20.1 | 35.0 | 66.3 | 20.1 | 39.1 | 54.1 | 25.7 | 48.2 | 66.3 | 28.8 | 50.8 | 65.1 | 25.7 | 62.6 | 65.1 | 57.9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 308.0 | 309.0 | 305.8 | 299.2 | 309.0 | 289.6 | 302.0 | 309.0 | 289.6 | 301.3 | 307.4 | 295.7 | 296.0 | 303.6 | 289.6 | 296.9 | 307.4 | 290.8 | 292.5 | 294.6 | 290.8 | 6.7 | 9 | 2 | 2.5 | 9 | 0 | 4.8 | 9 | 0 | 0.2 | 1 | 0 | 3.0 | 8.0 | 0.0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093.0 | 16093.0 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 5.5 | 5.9 | 4.6 | 3.3 | 5.9 | 1.1 | 3.9 | 5.9 | 1.4 | 3.8 | 5.2 | 2.1 | 2.2 | 3.8 | 1.4 | 2.7 | 5.2 | 1.4 | 1.5 | 1.9 | 1.4 | 13 |
| 2 | 20150701 | 286.1 | 286.5 | 285.5 | 285.8 | 288.0 | 283.8 | 285.4 | 286.5 | 283.8 | 287.8 | 288.4 | 286.8 | 284.8 | 285.5 | 283.8 | 287.5 | 288.6 | 286.4 | 287.2 | 288.6 | 286.4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 305.1 | 305.9 | 303.8 | 298.3 | 305.9 | 290.8 | 300.7 | 305.9 | 290.9 | 299.4 | 303.4 | 294.9 | 296.3 | 302.4 | 290.9 | 296.6 | 303.4 | 293.3 | 293.8 | 294.3 | 293.3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 31.4 | 32.6 | 28.9 | 48.1 | 66.1 | 28.9 | 40.7 | 66.1 | 28.9 | 50.6 | 66.5 | 36.5 | 50.0 | 66.1 | 31.6 | 58.1 | 71.5 | 36.5 | 65.7 | 71.5 | 63.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 305.1 | 305.9 | 303.8 | 298.3 | 305.9 | 290.8 | 300.7 | 305.9 | 290.9 | 299.2 | 303.4 | 294.9 | 296.3 | 302.4 | 290.9 | 296.5 | 303.4 | 293.3 | 293.8 | 294.3 | 293.3 | 7.3 | 10 | 3 | 2.7 | 10 | 0 | 5.3 | 10 | 0 | 0.2 | 1 | 0 | 3.3 | 8.0 | 0.0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093.0 | 16093.0 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 6.1 | 6.5 | 5.3 | 3.6 | 6.5 | 1.2 | 4.4 | 6.5 | 1.2 | 4.1 | 5.7 | 2.2 | 2.7 | 4.3 | 1.2 | 3.0 | 5.7 | 1.4 | 1.9 | 2.8 | 1.4 | 13 |
| 3 | 20150702 | 288.9 | 289.4 | 287.9 | 288.3 | 291.8 | 286.1 | 287.7 | 289.4 | 286.1 | 291.0 | 291.8 | 289.6 | 286.6 | 287.3 | 286.1 | 291.2 | 291.9 | 289.6 | 291.4 | 291.9 | 290.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 302.0 | 303.8 | 299.8 | 297.6 | 303.8 | 293.2 | 299.0 | 303.8 | 293.2 | 298.6 | 302.6 | 294.8 | 295.9 | 299.5 | 293.2 | 296.0 | 302.6 | 292.9 | 293.5 | 293.9 | 292.9 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6.3 | 7.3 | 5.7 | NaN | NaN | NaN | 6.1 | 7.3 | 5.5 | NaN | NaN | NaN | 6.0 | 7.3 | 5.3 | 5.3 | 5.3 | 5.3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 47.1 | 50.7 | 43.5 | 58.5 | 77.6 | 43.5 | 52.0 | 66.8 | 43.5 | 66.7 | 81.2 | 48.1 | 56.8 | 66.8 | 47.3 | 77.2 | 93.6 | 48.1 | 87.7 | 93.6 | 80.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 301.2 | 302.9 | 298.9 | 297.2 | 302.9 | 293.2 | 298.5 | 302.9 | 293.2 | 297.9 | 301.7 | 294.8 | 295.8 | 298.7 | 293.2 | 295.7 | 301.7 | 292.9 | 293.5 | 293.9 | 292.9 | 6.5 | 8 | 3 | 2.3 | 8 | 0 | 4.6 | 8 | 0 | 0.2 | 1 | 0 | 2.7 | 7.0 | 0.0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093.0 | 16093.0 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 4.0 | 4.8 | 2.8 | 3.0 | 5.4 | 1.2 | 2.8 | 4.8 | 1.2 | 4.5 | 5.4 | 3.5 | 1.7 | 2.3 | 1.2 | 3.6 | 5.4 | 2.4 | 2.7 | 3.4 | 2.4 | 13 |
| 4 | 20150703 | 289.1 | 290.4 | 288.5 | 290.5 | 293.0 | 287.8 | 290.6 | 293.0 | 288.5 | 289.2 | 290.9 | 287.8 | 292.1 | 293.0 | 290.9 | 288.9 | 290.9 | 287.4 | 288.6 | 289.6 | 287.4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 304.2 | 305.1 | 302.6 | 298.2 | 305.1 | 292.6 | 300.4 | 305.1 | 292.6 | 298.6 | 303.2 | 295.0 | 296.5 | 301.3 | 292.6 | 295.6 | 303.2 | 290.9 | 292.5 | 293.9 | 290.9 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.4 | 5.5 | 5.3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 42.5 | 51.4 | 39.1 | 67.5 | 98.7 | 39.1 | 61.4 | 98.7 | 39.1 | 58.3 | 73.9 | 40.0 | 80.3 | 98.7 | 57.9 | 68.2 | 80.8 | 40.0 | 78.1 | 80.8 | 75.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 303.4 | 304.2 | 301.4 | 297.8 | 304.2 | 292.6 | 299.7 | 304.2 | 292.6 | 298.4 | 302.8 | 295.0 | 296.0 | 299.8 | 292.6 | 295.5 | 302.8 | 290.9 | 292.5 | 293.9 | 290.9 | 7.3 | 10 | 3 | 2.6 | 10 | 0 | 5.2 | 10 | 0 | 0.2 | 1 | 0 | 3.0 | 8.0 | 0.0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 16093.0 | 16093.0 | 16093.0 | 16093.0 | 16093 | 16093 | 16093.0 | 16093 | 16093 | 5.9 | 6.5 | 5.3 | 4.2 | 6.5 | 2.3 | 4.7 | 6.5 | 2.3 | 4.4 | 6.0 | 3.1 | 3.5 | 5.0 | 2.3 | 3.1 | 6.0 | 1.5 | 1.9 | 2.2 | 1.5 | 13 |
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| 51175 | 20220626 | 284.0 | 285.0 | 283.0 | 285.0 | 288.0 | 283.0 | 285.0 | 286.0 | 283.0 | 288.0 | 288.0 | 287.0 | 286.0 | 286.0 | 285.0 | 287.0 | 288.0 | 286.0 | 287.0 | 288.0 | 286.0 | 0.7 | 0.8 | 0.6 | 0.3 | 0.8 | 0.0 | 0.5 | 0.8 | 0.0 | 0.2 | 0.5 | 0.0 | 0.3 | 0.6 | 0.0 | 0.1 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 | 303.0 | 304.0 | 301.0 | 296.0 | 304.0 | 287.0 | 298.0 | 304.0 | 288.0 | 297.0 | 301.0 | 293.0 | 294.0 | 300.0 | 288.0 | 294.0 | 301.0 | 289.0 | 290.0 | 292.0 | 289.0 | 895.0 | 999.0 | 679.0 | 357.0 | 999.0 | 0.0 | 637.0 | 999.0 | 1.2 | 154.0 | 498.0 | 0.0 | 379.0 | 806.0 | 1.2 | 77.3 | 498.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 100855.0 | 100990.0 | 100780.0 | 100980.0 | 101120.0 | 100780.0 | 100963.0 | 101120.0 | 100780.0 | 100973.0 | 101150.0 | 100800.0 | 101072.0 | 101120.0 | 101040.0 | 101058.0 | 101210.0 | 100800.0 | 101143.0 | 101210.0 | 101120.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 30.4 | 35.3 | 26.4 | 55.3 | 81.4 | 26.4 | 45.4 | 81.4 | 26.4 | 56.9 | 67.7 | 42.7 | 60.4 | 81.4 | 40.4 | 69.5 | 85.6 | 42.7 | 82.2 | 85.6 | 74.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 303.0 | 304.0 | 301.0 | 296.0 | 304.0 | 287.0 | 298.0 | 304.0 | 288.0 | 297.0 | 301.0 | 293.0 | 294.0 | 300.0 | 288.0 | 294.0 | 301.0 | 289.0 | 290.0 | 292.0 | 289.0 | 6.7 | 9 | 2 | 2.5 | 9 | 0 | 4.8 | 9 | 0 | 0.2 | 1 | 0 | 3.0 | 8.0 | 0.0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 13017.0 | 13790 | 12575 | 13359.0 | 14454 | 12514 | 13000.0 | 14454 | 12514 | 13539.0 | 13950 | 12722 | 12983.0 | 14454.0 | 12514.0 | 13441.0 | 13950 | 12722 | 13344.0 | 13926 | 12740 | 3.7 | 4.6 | 3.2 | 2.5 | 5.2 | 1.0 | 2.9 | 4.6 | 1.2 | 3.0 | 5.2 | 1.2 | 2.1 | 2.6 | 1.2 | 2.1 | 5.2 | 0.6 | 1.1 | 1.6 | 0.6 | 8 |
| 51176 | 20220627 | 289.0 | 289.0 | 288.0 | 288.0 | 290.0 | 286.0 | 289.0 | 290.0 | 288.0 | 290.0 | 290.0 | 288.0 | 289.0 | 290.0 | 288.0 | 289.0 | 290.0 | 287.0 | 288.0 | 289.0 | 287.0 | 0.5 | 0.5 | 0.5 | 0.2 | 0.5 | 0.0 | 0.3 | 0.5 | 0.0 | 0.1 | 0.4 | 0.0 | 0.2 | 0.4 | 0.0 | 0.1 | 0.4 | 0.0 | 0.0 | 0.0 | 0.0 | 297.0 | 298.0 | 296.0 | 294.0 | 298.0 | 289.0 | 295.0 | 298.0 | 289.0 | 294.0 | 297.0 | 291.0 | 293.0 | 295.0 | 289.0 | 291.0 | 297.0 | 287.0 | 289.0 | 290.0 | 287.0 | 772.0 | 846.0 | 636.0 | 304.0 | 846.0 | 0.0 | 536.0 | 846.0 | 1.0 | 145.0 | 473.0 | 0.0 | 301.0 | 657.0 | 1.0 | 72.5 | 473.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 101473.0 | 101510.0 | 101460.0 | 101405.0 | 101930.0 | 101120.0 | 101418.0 | 101510.0 | 101280.0 | 101778.0 | 101990.0 | 101540.0 | 101363.0 | 101440.0 | 101280.0 | 101848.0 | 101990.0 | 101540.0 | 101917.0 | 101950.0 | 101900.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.6 | 0.0 | 0.1 | 0.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 58.2 | 63.1 | 55.9 | 72.8 | 90.9 | 55.9 | 67.8 | 90.9 | 55.9 | 77.7 | 92.7 | 63.1 | 77.4 | 90.9 | 64.4 | 86.6 | 100.0 | 63.1 | 95.4 | 100.0 | 91.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 297.0 | 298.0 | 296.0 | 294.0 | 298.0 | 289.0 | 295.0 | 298.0 | 289.0 | 294.0 | 297.0 | 291.0 | 293.0 | 295.0 | 289.0 | 291.0 | 297.0 | 287.0 | 289.0 | 290.0 | 287.0 | 6.7 | 9 | 2 | 2.4 | 9 | 0 | 4.8 | 9 | 0 | 0.2 | 1 | 0 | 2.8 | 8.0 | 0.0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 12564.0 | 12846 | 11685 | 12205.0 | 13950 | 8046 | 11998.0 | 13857 | 9605 | 10498.0 | 12716 | 8046 | 11431.0 | 13857.0 | 9605.0 | 9272.0 | 12716 | 8046 | 8046.0 | 8046 | 8046 | 3.9 | 4.9 | 3.3 | 3.0 | 5.0 | 0.6 | 3.5 | 4.9 | 2.2 | 4.3 | 5.0 | 3.3 | 3.2 | 3.9 | 2.2 | 3.1 | 5.0 | 1.4 | 1.8 | 2.8 | 1.4 | 8 |
| 51177 | 20220628 | 283.0 | 285.0 | 282.0 | 286.0 | 290.0 | 282.0 | 284.0 | 287.0 | 282.0 | 287.0 | 288.0 | 286.0 | 286.0 | 287.0 | 284.0 | 287.0 | 288.0 | 285.0 | 286.0 | 288.0 | 285.0 | 0.7 | 0.7 | 0.6 | 0.3 | 0.7 | 0.0 | 0.4 | 0.7 | 0.0 | 0.2 | 0.5 | 0.0 | 0.2 | 0.5 | 0.0 | 0.1 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 | 302.0 | 303.0 | 301.0 | 295.0 | 303.0 | 287.0 | 298.0 | 303.0 | 287.0 | 297.0 | 301.0 | 293.0 | 294.0 | 299.0 | 287.0 | 293.0 | 301.0 | 288.0 | 290.0 | 292.0 | 288.0 | 900.0 | 1000.0 | 687.0 | 356.0 | 1000.0 | 0.0 | 633.0 | 1000.0 | 0.9 | 157.0 | 507.0 | 0.0 | 365.0 | 815.0 | 0.9 | 78.5 | 507.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 101630.0 | 101790.0 | 101510.0 | 101785.0 | 101990.0 | 101510.0 | 101767.0 | 101950.0 | 101510.0 | 101655.0 | 101780.0 | 101520.0 | 101903.0 | 101950.0 | 101850.0 | 101651.0 | 101780.0 | 101520.0 | 101647.0 | 101750.0 | 101570.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 29.4 | 33.4 | 27.1 | 62.4 | 100.0 | 27.1 | 47.5 | 98.0 | 27.1 | 56.2 | 74.0 | 38.1 | 65.5 | 98.0 | 40.0 | 68.3 | 81.8 | 38.1 | 80.5 | 81.8 | 78.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 302.0 | 303.0 | 301.0 | 295.0 | 303.0 | 287.0 | 298.0 | 303.0 | 287.0 | 297.0 | 301.0 | 293.0 | 294.0 | 299.0 | 287.0 | 293.0 | 301.0 | 288.0 | 290.0 | 292.0 | 288.0 | 6.7 | 9 | 2 | 2.5 | 9 | 0 | 4.8 | 9 | 0 | 0.2 | 1 | 0 | 3.0 | 8.0 | 0.0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 12907.0 | 12983 | 12681 | 11275.0 | 14599 | 8046 | 12089.0 | 12983 | 8046 | 13975.0 | 14639 | 13058 | 11271.0 | 12881.0 | 8046.0 | 13913.0 | 14639 | 13058 | 13850.0 | 14314 | 13497 | 3.2 | 4.3 | 2.0 | 2.7 | 5.0 | 1.2 | 2.5 | 4.3 | 1.2 | 3.6 | 5.0 | 1.9 | 1.8 | 2.5 | 1.2 | 2.4 | 5.0 | 1.0 | 1.3 | 1.7 | 1.0 | 8 |
| 51178 | 20220629 | 283.0 | 285.0 | 282.0 | 285.0 | 288.0 | 282.0 | 284.0 | 286.0 | 282.0 | 286.0 | 287.0 | 285.0 | 285.0 | 286.0 | 283.0 | 286.0 | 287.0 | 285.0 | 286.0 | 287.0 | 286.0 | 0.7 | 0.8 | 0.6 | 0.3 | 0.8 | 0.0 | 0.5 | 0.8 | 0.0 | 0.2 | 0.5 | 0.0 | 0.3 | 0.6 | 0.0 | 0.1 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 | 304.0 | 304.0 | 303.0 | 297.0 | 304.0 | 288.0 | 300.0 | 304.0 | 289.0 | 297.0 | 302.0 | 293.0 | 296.0 | 302.0 | 289.0 | 294.0 | 302.0 | 289.0 | 290.0 | 292.0 | 289.0 | 878.0 | 982.0 | 638.0 | 354.0 | 982.0 | 0.0 | 629.0 | 982.0 | 1.0 | 118.0 | 465.0 | 0.0 | 380.0 | 806.0 | 1.0 | 53.5 | 465.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 101237.0 | 101400.0 | 101100.0 | 101421.0 | 101780.0 | 101060.0 | 101388.0 | 101590.0 | 101100.0 | 101183.0 | 101320.0 | 101060.0 | 101540.0 | 101590.0 | 101440.0 | 101206.0 | 101320.0 | 101060.0 | 101228.0 | 101320.0 | 101150.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 28.6 | 33.6 | 25.4 | 53.1 | 81.8 | 25.4 | 39.9 | 79.8 | 25.4 | 51.0 | 67.3 | 36.2 | 51.2 | 79.8 | 30.1 | 64.0 | 83.1 | 36.2 | 77.1 | 83.1 | 72.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 304.0 | 304.0 | 303.0 | 297.0 | 304.0 | 288.0 | 300.0 | 304.0 | 289.0 | 297.0 | 302.0 | 293.0 | 296.0 | 302.0 | 289.0 | 294.0 | 302.0 | 289.0 | 290.0 | 292.0 | 289.0 | 6.7 | 9 | 2 | 2.5 | 9 | 0 | 4.8 | 9 | 0 | 0.2 | 1 | 0 | 3.0 | 8.0 | 0.0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 12906.0 | 12956 | 12822 | 13425.0 | 14639 | 12221 | 13014.0 | 14397 | 12221 | 13886.0 | 15023 | 12858 | 13121.0 | 14397.0 | 12221.0 | 14392.0 | 15184 | 12858 | 14897.0 | 15184 | 14084 | 4.9 | 5.9 | 3.3 | 3.0 | 5.9 | 1.0 | 3.6 | 5.9 | 1.3 | 3.9 | 5.4 | 2.5 | 2.2 | 3.1 | 1.3 | 2.7 | 5.4 | 1.3 | 1.6 | 1.9 | 1.3 | 8 |
| 51179 | 20220630 | 283.0 | 286.0 | 281.0 | 285.0 | 288.0 | 281.0 | 283.0 | 286.0 | 281.0 | 287.0 | 288.0 | 286.0 | 284.0 | 286.0 | 281.0 | 289.0 | 291.0 | 286.0 | 290.0 | 291.0 | 290.0 | 0.6 | 0.7 | 0.5 | 0.3 | 0.7 | 0.0 | 0.4 | 0.7 | 0.0 | 0.2 | 0.5 | 0.0 | 0.3 | 0.5 | 0.0 | 0.1 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 | 304.0 | 305.0 | 303.0 | 297.0 | 305.0 | 289.0 | 300.0 | 305.0 | 289.0 | 297.0 | 302.0 | 293.0 | 296.0 | 302.0 | 289.0 | 294.0 | 302.0 | 291.0 | 292.0 | 292.0 | 291.0 | 878.0 | 975.0 | 672.0 | 353.0 | 975.0 | 0.0 | 629.0 | 975.0 | 0.9 | 153.0 | 495.0 | 0.0 | 380.0 | 810.0 | 0.9 | 76.7 | 495.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 101007.0 | 101090.0 | 100940.0 | 101167.0 | 101440.0 | 100940.0 | 101108.0 | 101250.0 | 100940.0 | 101253.0 | 101510.0 | 100980.0 | 101210.0 | 101250.0 | 101140.0 | 101403.0 | 101590.0 | 100980.0 | 101553.0 | 101590.0 | 101530.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 27.2 | 33.9 | 23.6 | 51.7 | 83.1 | 23.6 | 38.1 | 83.1 | 23.6 | 54.8 | 75.5 | 38.8 | 49.0 | 83.1 | 26.5 | 73.1 | 95.1 | 38.8 | 91.3 | 95.1 | 88.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 304.0 | 305.0 | 303.0 | 297.0 | 305.0 | 289.0 | 300.0 | 305.0 | 289.0 | 297.0 | 302.0 | 293.0 | 296.0 | 302.0 | 289.0 | 294.0 | 302.0 | 291.0 | 292.0 | 292.0 | 291.0 | 6.7 | 9 | 2 | 2.5 | 9 | 0 | 4.8 | 9 | 0 | 0.2 | 1 | 0 | 3.0 | 8.0 | 0.0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 12812.0 | 13113 | 12563 | 13636.0 | 15184 | 12315 | 13031.0 | 14525 | 12315 | 13277.0 | 13846 | 12720 | 13250.0 | 14525.0 | 12315.0 | 10542.0 | 13846 | 6482 | 7808.0 | 9514 | 6482 | 2.7 | 3.9 | 1.1 | 2.2 | 4.4 | 0.8 | 2.0 | 3.9 | 0.8 | 3.1 | 4.4 | 2.2 | 1.2 | 1.6 | 0.8 | 3.4 | 4.4 | 2.2 | 3.6 | 3.9 | 2.9 | 8 |
51180 rows × 275 columns
Now we are going to change the format of the variable date into dates that look better and create 3 columns for the Year, Month and Day.
ETO["date"] = [i[:4] + "-" + i[4:6] + "-" + i[6:] for i in map(str,ETO.date)]
ETO[["Year", "Month", "Day"]] = ETO.date.str.split("-", expand = True)
ETO.loc[ETO.Month.isin([i for i in ETO.Month.unique() if int(i) <= 9]), "Month"] = [i.replace("0", "") for i in ETO.Month if int(i) < 10]
ETO.loc[ETO.Day.isin([i for i in ETO.Day.unique() if int(i) <= 9]), "Day"] = [i.replace("0", "") for i in ETO.Day if int(i) < 10]
Let's keep the columns that have the daily data and change the index.
ETO = (ETO.set_index(["ID_ESTACION", "Day", "Month", "Year"])
.filter(regex='.*LocalDay(?!time).*')).reset_index()
ETO
| ID_ESTACION | Day | Month | Year | DewpointLocalDayAvg | DewpointLocalDayMax | DewpointLocalDayMin | EvapotranspirationLocalDayAvg | EvapotranspirationLocalDayMax | EvapotranspirationLocalDayMin | FeelsLikeLocalDayAvg | FeelsLikeLocalDayMax | FeelsLikeLocalDayMin | GlobalHorizontalIrradianceLocalDayAvg | GlobalHorizontalIrradianceLocalDayMax | GlobalHorizontalIrradianceLocalDayMin | GustLocalDayAvg | GustLocalDayMax | GustLocalDayMin | MSLPLocalDayAvg | MSLPLocalDayMax | MSLPLocalDayMin | PrecipAmountLocalDayAvg | PrecipAmountLocalDayMax | PrecipAmountLocalDayMin | RelativeHumidityLocalDayAvg | RelativeHumidityLocalDayMax | RelativeHumidityLocalDayMin | SnowAmountLocalDayAvg | SnowAmountLocalDayMax | SnowAmountLocalDayMin | TemperatureLocalDayAvg | TemperatureLocalDayMax | TemperatureLocalDayMin | UVIndexLocalDayAvg | UVIndexLocalDayMax | UVIndexLocalDayMin | VisibilityLocalDayAvg | VisibilityLocalDayMax | VisibilityLocalDayMin | WindSpeedLocalDayAvg | WindSpeedLocalDayMax | WindSpeedLocalDayMin | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 13 | 29 | 6 | 2015 | 286.0 | 287.0 | 285.4 | NaN | NaN | NaN | 304.1 | 307.6 | 298.6 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 34.3 | 47.1 | 26.3 | 0.0 | 0.0 | 0 | 304.1 | 307.6 | 298.6 | 0.5 | 2 | 0 | 16093.0 | 16093 | 16093 | 4.0 | 5.2 | 2.3 |
| 1 | 13 | 30 | 6 | 2015 | 284.3 | 286.5 | 282.5 | NaN | NaN | NaN | 299.2 | 309.0 | 289.6 | NaN | NaN | NaN | 6.0 | 7.1 | 4.6 | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 43.6 | 69.7 | 20.1 | 0.0 | 0.0 | 0 | 299.2 | 309.0 | 289.6 | 2.5 | 9 | 0 | 16093.0 | 16093 | 16093 | 3.3 | 5.9 | 1.1 |
| 2 | 13 | 1 | 7 | 2015 | 285.8 | 288.0 | 283.8 | NaN | NaN | NaN | 298.3 | 305.9 | 290.8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 48.1 | 66.1 | 28.9 | 0.0 | 0.0 | 0 | 298.3 | 305.9 | 290.8 | 2.7 | 10 | 0 | 16093.0 | 16093 | 16093 | 3.6 | 6.5 | 1.2 |
| 3 | 13 | 2 | 7 | 2015 | 288.3 | 291.8 | 286.1 | NaN | NaN | NaN | 297.6 | 303.8 | 293.2 | NaN | NaN | NaN | 6.3 | 7.3 | 5.7 | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 58.5 | 77.6 | 43.5 | 0.0 | 0.0 | 0 | 297.2 | 302.9 | 293.2 | 2.3 | 8 | 0 | 16093.0 | 16093 | 16093 | 3.0 | 5.4 | 1.2 |
| 4 | 13 | 3 | 7 | 2015 | 290.5 | 293.0 | 287.8 | NaN | NaN | NaN | 298.2 | 305.1 | 292.6 | NaN | NaN | NaN | 5.4 | 5.5 | 5.3 | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 67.5 | 98.7 | 39.1 | 0.0 | 0.0 | 0 | 297.8 | 304.2 | 292.6 | 2.6 | 10 | 0 | 16093.0 | 16093 | 16093 | 4.2 | 6.5 | 2.3 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 51175 | 8 | 26 | 6 | 2022 | 285.0 | 288.0 | 283.0 | 0.3 | 0.8 | 0.0 | 296.0 | 304.0 | 287.0 | 357.0 | 999.0 | 0.0 | NaN | NaN | NaN | 100980.0 | 101120.0 | 100780.0 | 0.0 | 0.0 | 0.0 | 55.3 | 81.4 | 26.4 | 0.0 | 0.0 | 0 | 296.0 | 304.0 | 287.0 | 2.5 | 9 | 0 | 13359.0 | 14454 | 12514 | 2.5 | 5.2 | 1.0 |
| 51176 | 8 | 27 | 6 | 2022 | 288.0 | 290.0 | 286.0 | 0.2 | 0.5 | 0.0 | 294.0 | 298.0 | 289.0 | 304.0 | 846.0 | 0.0 | NaN | NaN | NaN | 101405.0 | 101930.0 | 101120.0 | 0.0 | 0.6 | 0.0 | 72.8 | 90.9 | 55.9 | 0.0 | 0.0 | 0 | 294.0 | 298.0 | 289.0 | 2.4 | 9 | 0 | 12205.0 | 13950 | 8046 | 3.0 | 5.0 | 0.6 |
| 51177 | 8 | 28 | 6 | 2022 | 286.0 | 290.0 | 282.0 | 0.3 | 0.7 | 0.0 | 295.0 | 303.0 | 287.0 | 356.0 | 1000.0 | 0.0 | NaN | NaN | NaN | 101785.0 | 101990.0 | 101510.0 | 0.0 | 0.0 | 0.0 | 62.4 | 100.0 | 27.1 | 0.0 | 0.0 | 0 | 295.0 | 303.0 | 287.0 | 2.5 | 9 | 0 | 11275.0 | 14599 | 8046 | 2.7 | 5.0 | 1.2 |
| 51178 | 8 | 29 | 6 | 2022 | 285.0 | 288.0 | 282.0 | 0.3 | 0.8 | 0.0 | 297.0 | 304.0 | 288.0 | 354.0 | 982.0 | 0.0 | NaN | NaN | NaN | 101421.0 | 101780.0 | 101060.0 | 0.0 | 0.0 | 0.0 | 53.1 | 81.8 | 25.4 | 0.0 | 0.0 | 0 | 297.0 | 304.0 | 288.0 | 2.5 | 9 | 0 | 13425.0 | 14639 | 12221 | 3.0 | 5.9 | 1.0 |
| 51179 | 8 | 30 | 6 | 2022 | 285.0 | 288.0 | 281.0 | 0.3 | 0.7 | 0.0 | 297.0 | 305.0 | 289.0 | 353.0 | 975.0 | 0.0 | NaN | NaN | NaN | 101167.0 | 101440.0 | 100940.0 | 0.0 | 0.0 | 0.0 | 51.7 | 83.1 | 23.6 | 0.0 | 0.0 | 0 | 297.0 | 305.0 | 289.0 | 2.5 | 9 | 0 | 13636.0 | 15184 | 12315 | 2.2 | 4.4 | 0.8 |
51180 rows × 43 columns
And now let us analyse the existence of missing data in our variables.
ETO.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 51180 entries, 0 to 51179 Data columns (total 43 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID_ESTACION 51180 non-null int64 1 Day 51180 non-null object 2 Month 51180 non-null object 3 Year 51180 non-null object 4 DewpointLocalDayAvg 51180 non-null float64 5 DewpointLocalDayMax 51180 non-null float64 6 DewpointLocalDayMin 51180 non-null float64 7 EvapotranspirationLocalDayAvg 16600 non-null float64 8 EvapotranspirationLocalDayMax 16600 non-null float64 9 EvapotranspirationLocalDayMin 16600 non-null float64 10 FeelsLikeLocalDayAvg 51180 non-null float64 11 FeelsLikeLocalDayMax 51180 non-null float64 12 FeelsLikeLocalDayMin 51180 non-null float64 13 GlobalHorizontalIrradianceLocalDayAvg 16600 non-null float64 14 GlobalHorizontalIrradianceLocalDayMax 16600 non-null float64 15 GlobalHorizontalIrradianceLocalDayMin 16600 non-null float64 16 GustLocalDayAvg 14415 non-null float64 17 GustLocalDayMax 14415 non-null float64 18 GustLocalDayMin 14415 non-null float64 19 MSLPLocalDayAvg 36200 non-null float64 20 MSLPLocalDayMax 36200 non-null float64 21 MSLPLocalDayMin 36200 non-null float64 22 PrecipAmountLocalDayAvg 51180 non-null float64 23 PrecipAmountLocalDayMax 51180 non-null float64 24 PrecipAmountLocalDayMin 51180 non-null float64 25 RelativeHumidityLocalDayAvg 51180 non-null float64 26 RelativeHumidityLocalDayMax 51180 non-null float64 27 RelativeHumidityLocalDayMin 51180 non-null float64 28 SnowAmountLocalDayAvg 51180 non-null float64 29 SnowAmountLocalDayMax 51180 non-null float64 30 SnowAmountLocalDayMin 51180 non-null int64 31 TemperatureLocalDayAvg 51180 non-null float64 32 TemperatureLocalDayMax 51180 non-null float64 33 TemperatureLocalDayMin 51180 non-null float64 34 UVIndexLocalDayAvg 51180 non-null float64 35 UVIndexLocalDayMax 51180 non-null int64 36 UVIndexLocalDayMin 51180 non-null int64 37 VisibilityLocalDayAvg 51180 non-null float64 38 VisibilityLocalDayMax 51180 non-null int64 39 VisibilityLocalDayMin 51180 non-null int64 40 WindSpeedLocalDayAvg 51180 non-null float64 41 WindSpeedLocalDayMax 51180 non-null float64 42 WindSpeedLocalDayMin 51180 non-null float64 dtypes: float64(34), int64(6), object(3) memory usage: 16.8+ MB
ETO.describe()
| ID_ESTACION | DewpointLocalDayAvg | DewpointLocalDayMax | DewpointLocalDayMin | EvapotranspirationLocalDayAvg | EvapotranspirationLocalDayMax | EvapotranspirationLocalDayMin | FeelsLikeLocalDayAvg | FeelsLikeLocalDayMax | FeelsLikeLocalDayMin | GlobalHorizontalIrradianceLocalDayAvg | GlobalHorizontalIrradianceLocalDayMax | GlobalHorizontalIrradianceLocalDayMin | GustLocalDayAvg | GustLocalDayMax | GustLocalDayMin | MSLPLocalDayAvg | MSLPLocalDayMax | MSLPLocalDayMin | PrecipAmountLocalDayAvg | PrecipAmountLocalDayMax | PrecipAmountLocalDayMin | RelativeHumidityLocalDayAvg | RelativeHumidityLocalDayMax | RelativeHumidityLocalDayMin | SnowAmountLocalDayAvg | SnowAmountLocalDayMax | SnowAmountLocalDayMin | TemperatureLocalDayAvg | TemperatureLocalDayMax | TemperatureLocalDayMin | UVIndexLocalDayAvg | UVIndexLocalDayMax | UVIndexLocalDayMin | VisibilityLocalDayAvg | VisibilityLocalDayMax | VisibilityLocalDayMin | WindSpeedLocalDayAvg | WindSpeedLocalDayMax | WindSpeedLocalDayMin | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 16600.000000 | 16600.000000 | 16600.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 16600.000000 | 16600.000000 | 16600.0 | 14415.000000 | 14415.000000 | 14415.000000 | 36200.000000 | 36200.000000 | 36200.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.0 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.0 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 | 51180.000000 |
| mean | 9.500000 | 281.334281 | 283.782683 | 278.840741 | 0.148729 | 0.435831 | 0.000476 | 287.853298 | 294.109048 | 282.337888 | 215.286355 | 704.958729 | 0.0 | 8.445522 | 9.809067 | 7.183580 | 101775.754633 | 102012.040884 | 101554.076519 | 0.065903 | 0.495397 | 0.001458 | 66.909105 | 87.593103 | 43.377737 | 0.000007 | 0.000033 | 0.0 | 288.529439 | 294.480045 | 283.347306 | 1.358054 | 5.634682 | 0.0 | 12873.402044 | 14737.164713 | 10321.934857 | 2.915457 | 4.817522 | 1.323185 |
| std | 5.766338 | 5.779998 | 5.698248 | 6.048484 | 0.091599 | 0.206679 | 0.006882 | 7.672332 | 8.746639 | 6.937972 | 95.095702 | 233.239807 | 0.0 | 1.802888 | 2.941010 | 1.242222 | 624.716678 | 591.425803 | 659.088147 | 0.301532 | 2.070037 | 0.046220 | 14.906146 | 12.223877 | 17.524015 | 0.000202 | 0.000896 | 0.0 | 6.863879 | 7.965405 | 6.097857 | 0.803475 | 2.850148 | 0.0 | 2347.294792 | 1509.757098 | 3941.034309 | 1.507220 | 2.033228 | 1.086706 |
| min | 0.000000 | 261.000000 | 264.000000 | 256.000000 | 0.000000 | 0.100000 | 0.000000 | 266.000000 | 269.000000 | 261.900000 | 16.600000 | 74.100000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 99276.000000 | 99660.000000 | 98860.000000 | 0.000000 | 0.000000 | 0.000000 | 22.300000 | 37.300000 | 6.500000 | 0.000000 | 0.000000 | 0.0 | 271.000000 | 273.000000 | 265.300000 | 0.100000 | 1.000000 | 0.0 | 453.000000 | 804.000000 | 96.000000 | 0.500000 | 1.000000 | 0.000000 |
| 25% | 4.750000 | 277.000000 | 280.000000 | 274.700000 | 0.100000 | 0.300000 | 0.000000 | 281.400000 | 287.000000 | 277.000000 | 130.000000 | 522.000000 | 0.0 | 7.500000 | 7.700000 | 7.200000 | 101406.150000 | 101640.000000 | 101180.000000 | 0.000000 | 0.000000 | 0.000000 | 56.400000 | 79.700000 | 30.500000 | 0.000000 | 0.000000 | 0.0 | 283.000000 | 288.000000 | 278.500000 | 0.500000 | 3.000000 | 0.0 | 11598.075000 | 14031.000000 | 8046.000000 | 1.900000 | 3.400000 | 0.600000 |
| 50% | 9.500000 | 281.200000 | 283.700000 | 279.000000 | 0.100000 | 0.400000 | 0.000000 | 287.100000 | 294.000000 | 282.000000 | 214.550000 | 733.700000 | 0.0 | 8.300000 | 9.200000 | 7.400000 | 101738.000000 | 101960.000000 | 101540.000000 | 0.000000 | 0.000000 | 0.000000 | 66.500000 | 90.900000 | 40.500000 | 0.000000 | 0.000000 | 0.0 | 287.900000 | 294.000000 | 283.000000 | 1.300000 | 6.000000 | 0.0 | 13236.000000 | 14895.000000 | 10982.000000 | 2.500000 | 4.400000 | 1.000000 |
| 75% | 14.250000 | 286.000000 | 288.300000 | 283.300000 | 0.200000 | 0.600000 | 0.000000 | 294.700000 | 301.400000 | 288.300000 | 307.000000 | 924.225000 | 0.0 | 9.400000 | 11.500000 | 7.700000 | 102157.000000 | 102370.000000 | 101960.000000 | 0.000000 | 0.000000 | 0.000000 | 77.500000 | 98.900000 | 52.900000 | 0.000000 | 0.000000 | 0.0 | 294.600000 | 301.000000 | 288.500000 | 2.200000 | 9.000000 | 0.0 | 14189.000000 | 16093.000000 | 13012.000000 | 3.400000 | 5.700000 | 1.600000 |
| max | 19.000000 | 295.000000 | 296.400000 | 294.000000 | 0.500000 | 1.100000 | 0.100000 | 306.500000 | 316.300000 | 301.100000 | 393.000000 | 1026.000000 | 0.0 | 19.400000 | 26.100000 | 15.400000 | 103557.000000 | 103700.000000 | 103420.000000 | 8.100000 | 60.300000 | 2.500000 | 100.000000 | 100.000000 | 100.000000 | 0.010000 | 0.050000 | 0.0 | 306.200000 | 316.300000 | 300.200000 | 3.000000 | 10.000000 | 0.0 | 16093.000000 | 16093.000000 | 16093.000000 | 12.900000 | 16.300000 | 10.800000 |
columns_with_missing_data_ETO = ETO.isnull().sum()[ETO.isnull().sum() > 0].index
ETO.groupby('Year')[columns_with_missing_data_ETO].apply(lambda x: x.isnull().sum())
| EvapotranspirationLocalDayAvg | EvapotranspirationLocalDayMax | EvapotranspirationLocalDayMin | GlobalHorizontalIrradianceLocalDayAvg | GlobalHorizontalIrradianceLocalDayMax | GlobalHorizontalIrradianceLocalDayMin | GustLocalDayAvg | GustLocalDayMax | GustLocalDayMin | MSLPLocalDayAvg | MSLPLocalDayMax | MSLPLocalDayMin | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year | ||||||||||||
| 2015 | 3720 | 3720 | 3720 | 3720 | 3720 | 3720 | 2540 | 2540 | 2540 | 3720 | 3720 | 3720 |
| 2016 | 7320 | 7320 | 7320 | 7320 | 7320 | 7320 | 5095 | 5095 | 5095 | 7320 | 7320 | 7320 |
| 2017 | 7300 | 7300 | 7300 | 7300 | 7300 | 7300 | 5777 | 5777 | 5777 | 3940 | 3940 | 3940 |
| 2018 | 7300 | 7300 | 7300 | 7300 | 7300 | 7300 | 5720 | 5720 | 5720 | 0 | 0 | 0 |
| 2019 | 7300 | 7300 | 7300 | 7300 | 7300 | 7300 | 4787 | 4787 | 4787 | 0 | 0 | 0 |
| 2020 | 1260 | 1260 | 1260 | 1260 | 1260 | 1260 | 5101 | 5101 | 5101 | 0 | 0 | 0 |
| 2021 | 380 | 380 | 380 | 380 | 380 | 380 | 5239 | 5239 | 5239 | 0 | 0 | 0 |
| 2022 | 0 | 0 | 0 | 0 | 0 | 0 | 2506 | 2506 | 2506 | 0 | 0 | 0 |
sns.heatmap(ETO[columns_with_missing_data_ETO].isnull(), cbar=False);
fig, ax = plt.subplots(figsize=(28, 60))
g = sns.barplot(y=ETO[columns_with_missing_data_ETO].columns, x=ETO[columns_with_missing_data_ETO].isnull().sum(), orient = 'h', palette = 'Blues')
We can see that we have missing data in "EvapotranspirationLocalDayAvg", "EvapotranspirationLocalDayMax", "EvapotranspirationLocalDayMin", "GlobalHorizontalIrradianceLocalDayAvg", "GlobalHorizontalIrradianceLocalDayMax", "GlobalHorizontalIrradianceLocalDayMin", "GustLocalDayAvg", "GustLocalDayMax", "GustLocalDayMin", "MSLPLocalDayAvg", "MSLPLocalDayMax", "MSLPLocalDayMin".
sns.set_theme(style="white")
columns = [col for col in ETO.columns if ('Avg' in col[-3:])]
g = sns.PairGrid(ETO[columns], diag_sharey=False)
g.map_upper(sns.scatterplot, s=15)
g.map_lower(sns.scatterplot, s=15)
g.map_diag(sns.kdeplot, lw=2)
plt.show()
correlation_matrix = ETO[columns].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='Blues', annot_kws={"size": 8})
plt.show()
variable_columns = {
'DewpointLocalDay': ['DewpointLocalDayMax', 'DewpointLocalDayAvg', 'DewpointLocalDayMin'],
'EvapotranspirationLocalDay': ['EvapotranspirationLocalDayMax', 'EvapotranspirationLocalDayAvg', 'EvapotranspirationLocalDayMin'],
'FeelsLikeLocalDay': ['FeelsLikeLocalDayMax', 'FeelsLikeLocalDayAvg', 'FeelsLikeLocalDayMin'],
'GlobalHorizontalIrradianceLocalDay': ['GlobalHorizontalIrradianceLocalDayMax', 'GlobalHorizontalIrradianceLocalDayAvg', 'GlobalHorizontalIrradianceLocalDayMin'],
'GustLocalDay': ['GustLocalDayMax', 'GustLocalDayAvg', 'GustLocalDayMin'],
'MSLPLocalDay': ['MSLPLocalDayMax', 'MSLPLocalDayAvg', 'MSLPLocalDayMin'],
'PrecipAmountLocalDay': ['PrecipAmountLocalDayMax', 'PrecipAmountLocalDayAvg', 'PrecipAmountLocalDayMin'],
'RelativeHumidityLocalDay': ['RelativeHumidityLocalDayMax', 'RelativeHumidityLocalDayAvg', 'RelativeHumidityLocalDayMin'],
'SnowAmountLocalDay': ['SnowAmountLocalDayMax', 'SnowAmountLocalDayAvg', 'SnowAmountLocalDayMin'],
'TemperatureLocalDay': ['TemperatureLocalDayMax', 'TemperatureLocalDayAvg', 'TemperatureLocalDayMin'],
'UVIndexLocalDay': ['UVIndexLocalDayMax', 'UVIndexLocalDayAvg', 'UVIndexLocalDayMin'],
'VisibilityLocalDay': ['VisibilityLocalDayMax', 'VisibilityLocalDayAvg', 'VisibilityLocalDayMin'],
'WindSpeedLocalDay': ['WindSpeedLocalDayMax', 'WindSpeedLocalDayAvg', 'WindSpeedLocalDayMin']
}
statistics = ["Max", "Avg", "Min"]
fig, axes = plt.subplots(nrows=len(variable_columns), ncols=len(statistics), figsize=(20, 35)
)
for i_row, (axes_row, variable) in enumerate(zip(axes, variable_columns.keys())):
for ax, stat in zip(axes_row, statistics):
eto_column = variable + stat
try:
data = ETO[eto_column]
except KeyError:
ax.axis("off")
continue
sns.histplot(data=data, kde=True, ax = ax)
ax.set_title(eto_column)
ax.set_xlabel("Value")
ax.set_ylabel("Frequency")
plt.tight_layout()
plt.show()
ETO_MISS = ETO.groupby('Year')[columns_with_missing_data_ETO].apply(lambda x: x.isnull().sum())
for i, row in ETO_MISS.iterrows():
ETO_MISS.loc[i,'TAMAÑO'] = len(ETO[ETO.Year == i])
ETO_MISS
| EvapotranspirationLocalDayAvg | EvapotranspirationLocalDayMax | EvapotranspirationLocalDayMin | GlobalHorizontalIrradianceLocalDayAvg | GlobalHorizontalIrradianceLocalDayMax | GlobalHorizontalIrradianceLocalDayMin | GustLocalDayAvg | GustLocalDayMax | GustLocalDayMin | MSLPLocalDayAvg | MSLPLocalDayMax | MSLPLocalDayMin | TAMAÑO | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year | |||||||||||||
| 2015 | 3720 | 3720 | 3720 | 3720 | 3720 | 3720 | 2540 | 2540 | 2540 | 3720 | 3720 | 3720 | 3720.0 |
| 2016 | 7320 | 7320 | 7320 | 7320 | 7320 | 7320 | 5095 | 5095 | 5095 | 7320 | 7320 | 7320 | 7320.0 |
| 2017 | 7300 | 7300 | 7300 | 7300 | 7300 | 7300 | 5777 | 5777 | 5777 | 3940 | 3940 | 3940 | 7300.0 |
| 2018 | 7300 | 7300 | 7300 | 7300 | 7300 | 7300 | 5720 | 5720 | 5720 | 0 | 0 | 0 | 7300.0 |
| 2019 | 7300 | 7300 | 7300 | 7300 | 7300 | 7300 | 4787 | 4787 | 4787 | 0 | 0 | 0 | 7300.0 |
| 2020 | 1260 | 1260 | 1260 | 1260 | 1260 | 1260 | 5101 | 5101 | 5101 | 0 | 0 | 0 | 7320.0 |
| 2021 | 380 | 380 | 380 | 380 | 380 | 380 | 5239 | 5239 | 5239 | 0 | 0 | 0 | 7300.0 |
| 2022 | 0 | 0 | 0 | 0 | 0 | 0 | 2506 | 2506 | 2506 | 0 | 0 | 0 | 3620.0 |
%%time
ETO['Fecha'] = pd.to_datetime(ETO[['Year','Month','Day']])
ETO['fechas_invertida'] = ETO['Fecha'][::-1].reset_index(drop=True)
grupos = ETO.groupby('ID_ESTACION')
dataframes = {}
for estacion, grupo in grupos:
dataframes[estacion] = grupo.drop(grupo.index[0]).reset_index(level=0, drop=True)
dataframes_completos = {}
for estacion, df in dataframes.items():
columns_aus = df.columns[df.isnull().any()].tolist()
df_completo = df.copy(deep = True)
for col in columns_aus:
index_fechas_faltantes = df[df[col].isnull()].index.tolist()
df_prophet_train = df[~df[col].isnull()].rename(columns={'fechas_invertida': 'ds', col: 'y'})[['ds', 'y']]
predict = df[df[col].isnull()].rename(columns={'fechas_invertida': 'ds', col: 'y'})[['ds', 'y']]
model = Prophet()
model.fit(df_prophet_train)
if predict.empty:
pass
else:
forecast = model.predict(predict)
df_completo[col] = df_completo[col].fillna(df_completo['fechas_invertida'].map(forecast.set_index('ds')['yhat']))
fig = model.plot(forecast)
dataframes_completos[estacion] = df_completo.drop(df_completo.index[0]).reset_index(level=0, drop=True)
ETO_PROPHET = pd.concat(dataframes_completos.values(), ignore_index=True)
ETO_PROPHET = ETO_PROPHET.drop(['fechas_invertida'], axis = 1)
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Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). 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CPU times: total: 36.3 s Wall time: 1min 57s
ETO_PROPHET.groupby('Year')[columns_with_missing_data_ETO].apply(lambda x: x.isnull().sum())
| EvapotranspirationLocalDayAvg | EvapotranspirationLocalDayMax | EvapotranspirationLocalDayMin | GlobalHorizontalIrradianceLocalDayAvg | GlobalHorizontalIrradianceLocalDayMax | GlobalHorizontalIrradianceLocalDayMin | GustLocalDayAvg | GustLocalDayMax | GustLocalDayMin | MSLPLocalDayAvg | MSLPLocalDayMax | MSLPLocalDayMin | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year | ||||||||||||
| 2015 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2016 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2017 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2018 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2019 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2021 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
We can see that the data have been generated correctly and we are going to look at their distribution in case any transformation of the data is necessary.
ETO_FINAL = ETO_PROPHET.copy(deep = True)
for col in columns_with_missing_data_ETO:
if (ETO[col].min() == 0) & (ETO_FINAL[col].min() < 0):
ETO_FINAL[col] = ETO_FINAL[col].apply(lambda x: 0 if x < 0 else x)
plt.figure(figsize=(8,4))
sns.histplot(x=ETO[col], kde=True)
sns.histplot(x=ETO_FINAL[col], kde=True)
plt.show()
ETO_FINAL.to_csv('./Datos_Completos/DATOS_ETO_FINAL.csv', index=None)
ETO_FINAL
| ID_ESTACION | Day | Month | Year | DewpointLocalDayAvg | DewpointLocalDayMax | DewpointLocalDayMin | EvapotranspirationLocalDayAvg | EvapotranspirationLocalDayMax | EvapotranspirationLocalDayMin | FeelsLikeLocalDayAvg | FeelsLikeLocalDayMax | FeelsLikeLocalDayMin | GlobalHorizontalIrradianceLocalDayAvg | GlobalHorizontalIrradianceLocalDayMax | GlobalHorizontalIrradianceLocalDayMin | GustLocalDayAvg | GustLocalDayMax | GustLocalDayMin | MSLPLocalDayAvg | MSLPLocalDayMax | MSLPLocalDayMin | PrecipAmountLocalDayAvg | PrecipAmountLocalDayMax | PrecipAmountLocalDayMin | RelativeHumidityLocalDayAvg | RelativeHumidityLocalDayMax | RelativeHumidityLocalDayMin | SnowAmountLocalDayAvg | SnowAmountLocalDayMax | SnowAmountLocalDayMin | TemperatureLocalDayAvg | TemperatureLocalDayMax | TemperatureLocalDayMin | UVIndexLocalDayAvg | UVIndexLocalDayMax | UVIndexLocalDayMin | VisibilityLocalDayAvg | VisibilityLocalDayMax | VisibilityLocalDayMin | WindSpeedLocalDayAvg | WindSpeedLocalDayMax | WindSpeedLocalDayMin | Fecha | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 7 | 2015 | 287.0 | 289.0 | 285.0 | 0.290284 | 0.764094 | 0.002100 | 298.0 | 304.0 | 291.0 | 378.722425 | 1087.637213 | 0.0 | 6.077841 | 6.816106 | 5.000474 | 101477.688944 | 101793.123089 | 101386.831260 | 0.0 | 0.0 | 0.0 | 52.7 | 68.7 | 34.7 | 0.0 | 0.0 | 0 | 298.0 | 304.0 | 291.0 | 2.8 | 10 | 0 | 16093.0 | 16093 | 16093 | 3.5 | 6.7 | 1.0 | 2015-07-01 |
| 1 | 0 | 2 | 7 | 2015 | 289.0 | 292.0 | 287.0 | 0.286786 | 0.776297 | 0.002372 | 297.0 | 303.0 | 293.0 | 383.922731 | 1098.553161 | 0.0 | 5.900000 | 5.900000 | 5.900000 | 101483.675822 | 101792.554493 | 101395.078487 | 0.0 | 0.0 | 0.0 | 63.5 | 77.0 | 50.2 | 0.0 | 0.0 | 0 | 297.0 | 302.0 | 293.0 | 2.2 | 9 | 0 | 16093.0 | 16093 | 16093 | 3.0 | 5.2 | 1.1 | 2015-07-02 |
| 2 | 0 | 3 | 7 | 2015 | 291.0 | 293.0 | 288.0 | 0.295327 | 0.771802 | 0.003496 | 298.0 | 303.0 | 294.0 | 379.653810 | 1083.890569 | 0.0 | 5.800000 | 5.800000 | 5.800000 | 101468.005454 | 101779.772492 | 101376.823678 | 0.0 | 0.0 | 0.0 | 70.7 | 96.2 | 45.1 | 0.0 | 0.0 | 0 | 297.0 | 302.0 | 294.0 | 2.6 | 10 | 0 | 16093.0 | 16093 | 16093 | 4.1 | 6.4 | 2.2 | 2015-07-03 |
| 3 | 0 | 4 | 7 | 2015 | 288.0 | 291.0 | 286.0 | 0.295483 | 0.781390 | 0.002889 | 298.0 | 304.0 | 291.0 | 383.891758 | 1092.450319 | 0.0 | 6.546586 | 7.605024 | 5.183511 | 101482.748414 | 101792.490192 | 101391.985130 | 0.0 | 0.0 | 0.0 | 59.5 | 83.4 | 33.1 | 0.0 | 0.0 | 0 | 298.0 | 304.0 | 291.0 | 2.5 | 10 | 0 | 16093.0 | 16093 | 16093 | 3.2 | 5.5 | 1.5 | 2015-07-04 |
| 4 | 0 | 5 | 7 | 2015 | 287.0 | 290.0 | 285.0 | 0.292981 | 0.780724 | 0.003949 | 300.0 | 310.0 | 290.0 | 384.936384 | 1105.077884 | 0.0 | 6.397455 | 7.586672 | 5.175269 | 101488.925750 | 101802.431451 | 101380.537140 | 0.0 | 0.0 | 0.0 | 51.8 | 84.6 | 24.6 | 0.0 | 0.0 | 0 | 300.0 | 310.0 | 290.0 | 2.7 | 10 | 0 | 16093.0 | 16093 | 16093 | 2.8 | 5.2 | 0.7 | 2015-07-05 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 51135 | 19 | 26 | 6 | 2022 | 287.0 | 289.0 | 284.0 | 0.300000 | 0.700000 | 0.000000 | 296.0 | 304.0 | 288.0 | 354.000000 | 987.000000 | 0.0 | 8.315059 | 9.095765 | 7.767655 | 100975.000000 | 101120.000000 | 100800.000000 | 0.0 | 0.0 | 0.0 | 58.8 | 79.4 | 32.4 | 0.0 | 0.0 | 0 | 296.0 | 304.0 | 288.0 | 2.5 | 9 | 0 | 13471.0 | 15059 | 12258 | 2.1 | 3.8 | 0.8 | 2022-06-26 |
| 51136 | 19 | 27 | 6 | 2022 | 290.0 | 291.0 | 288.0 | 0.200000 | 0.500000 | 0.000000 | 295.0 | 298.0 | 291.0 | 285.000000 | 836.000000 | 0.0 | 8.264504 | 8.873532 | 7.715101 | 101399.000000 | 101920.000000 | 101110.000000 | 0.0 | 0.0 | 0.0 | 74.9 | 92.7 | 57.4 | 0.0 | 0.0 | 0 | 295.0 | 298.0 | 291.0 | 2.3 | 9 | 0 | 11678.0 | 14030 | 8046 | 3.2 | 4.9 | 0.7 | 2022-06-27 |
| 51137 | 19 | 28 | 6 | 2022 | 287.0 | 291.0 | 283.0 | 0.300000 | 0.700000 | 0.000000 | 296.0 | 305.0 | 289.0 | 352.000000 | 993.000000 | 0.0 | 7.895230 | 8.124502 | 7.687772 | 101771.000000 | 101980.000000 | 101510.000000 | 0.0 | 0.0 | 0.0 | 62.3 | 100.0 | 27.8 | 0.0 | 0.0 | 0 | 296.0 | 305.0 | 289.0 | 2.4 | 9 | 0 | 11412.0 | 14706 | 8046 | 2.5 | 4.6 | 1.0 | 2022-06-28 |
| 51138 | 19 | 29 | 6 | 2022 | 286.0 | 289.0 | 282.0 | 0.300000 | 0.800000 | 0.000000 | 298.0 | 306.0 | 290.0 | 355.000000 | 984.000000 | 0.0 | 7.987187 | 8.313957 | 7.635171 | 101406.000000 | 101750.000000 | 101050.000000 | 0.0 | 0.0 | 0.0 | 51.6 | 81.8 | 24.7 | 0.0 | 0.0 | 0 | 298.0 | 306.0 | 290.0 | 2.4 | 9 | 0 | 13741.0 | 15063 | 12426 | 3.0 | 5.6 | 1.1 | 2022-06-29 |
| 51139 | 19 | 30 | 6 | 2022 | 287.0 | 289.0 | 284.0 | 0.300000 | 0.700000 | 0.000000 | 298.0 | 305.0 | 290.0 | 351.000000 | 971.000000 | 0.0 | 8.047332 | 8.431844 | 7.818192 | 101160.000000 | 101430.000000 | 100940.000000 | 0.0 | 0.0 | 0.0 | 53.1 | 76.7 | 28.0 | 0.0 | 0.0 | 0 | 298.0 | 305.0 | 290.0 | 2.4 | 9 | 0 | 13775.0 | 15414 | 12471 | 1.9 | 3.1 | 0.8 | 2022-06-30 |
51140 rows × 44 columns